Multi-Objective Scheduling of LLM-Based Multi-Agent Workflows in Cloud Computing

Large language models (LLMs) have been increasingly composed into multi-agent workflows to accomplish complex and multi-step tasks. As the open-weight models continue to close the performance gap with the proprietary ones, model parameters are becoming a less significant source of competitive advantage in the artificial intelligence market. Yet how these models are efficiently scheduled onto distributed infrastructure such as the cloud, with respect to overall latency, monetary cost and resource utilization, remains largely unaddressed in the literature despite being an increasingly critical problem. Existing agentic workflow frameworks mostly focus on output quality and task success rates over popular benchmarks, rarely accounting for execution dynamics of LLM-based agents such as inference latency, computation power of heterogeneous resources, cloud-specific billing period and resource provisioning overheads. Therefore, this paper formulates multi-agent scheduling in cloud computing as a multi-objective optimization problem that jointly minimizes makespan and cost while maximizing resource utilization. Building on this formulation, we propose MO-SAIC (Multi-Objective Scheduling of Multi-Agent Workflows in Cloud Computing), a scheduling framework comprising four multi-objective schedulers with Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Weighted-Sum Genetic Algorithm (W-GA), a multi-objective adaptation of Heterogeneous Earliest Finish Time (MO-HEFT) and a random baseline (RAND). We evaluate the schedulers from the workflow scheduling, multi-objective optimization and system perspectives to measure their overall performance, quality of their Pareto-optimal fronts and impacts of workflow topology, cloud resource composition and scalability parameters. We report the experimental results with their statistical significance via the Wilcoxon test.